Atrophic Gastritis, Early Gastric Cancer, Gastric Cancer, Helicobacter Pylori Infection, High Grade Intraepithelial Neoplasia, Intestinal Metaplasia, Low Grade Intraepithelial Neoplasia
Conditions
Brief summary
The purpose of this study is to develop and validate a clinical decision support system based on automated algorithms. This system can use natural language processing to extract data from patients' endoscopic reports and pathological reports, identify patients' disease types and grades, and generate guidelines based follow-up or treatment recommendations
Interventions
OTHERAI recongnize disease and generate recommendations
According the endoscopic reports and pathological reports, the decision support system recognise patients' disease types and grades, and generate guidelines based survilliance or treatment recommendations.
Sponsors
Xiuli Zuo
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Inclusion criteria
* Patients aged 18 - 80 years * Patients underwent endoscopic examination
Exclusion criteria
* Patients with the contraindications to endoscopic examination * Patients with imcomplete examination information * Patients undergo endoscopy for therapy * Patients have history of upper gastrointestinal surgery * Patients with duodenal or Laryngeal neoplasms * Patients with gastrointestinal submucosal tumor
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The diagnostic accuracy of gastric diseases with deep learning algorithm | 12 month | The diagnostic accuracy of gastric diseases with deep learning algorithm |
| The accuracy of recommentions for different disease with deep learning algorithm | 12 month | The accuracy of recommentions for different disease with deep learning algorithm |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The diagnostic positive predictive value of gastric diseases with deep learning algorithm | 12 month | The diagnostic positive predictive valu of gastric diseases with deep learning algorithm |
| The diagnostic sensitivity of gastric diseases with deep learning algorithm | 12 month | The diagnostic sensitivity of gastric diseases with deep learning algorithm |
| The F-score of gastric diseases with deep learning algorithm | 12 month | The F-score of gastric diseases with deep learning algorithm |
| The diagnostic negative predictive value of gastric diseases with deep learning algorithm | 12 month | The diagnostic negative predictive value of gastric diseases with deep learning algorithm |
| The diagnostic specificity of gastric diseases with deep learning algorithm | 12 month | The diagnostic specificity of gastric diseases with deep learning algorithm |
Countries
China
Outcome results
None listed